Method and apparatus for seismic signal processing and exploration

ABSTRACT

A method, a map and an article of manufacture for the exploration of hydrocarbons. In one embodiment of the invention, the method comprises the steps of: accessing  3 D seismic data; dividing the data into an array of relatively small three-dimensional cells; determining in each cell the semblance/similarity, the dip and dip azimuth of the seismic traces contained therein; and displaying dip, dip azimuth and the semblance/similarity of each cell in the form a two-dimensional map. In one embodiment, semblance/similarity is a function of time, the number of seismic traces within the cell, and the apparent dip and apparent dip azimuth of the traces within the cell; the semblance/similarity of a cell is determined by making a plurality of measurements of the semblance/similarity of the traces within the cell and selecting the largest of the measurements. In addition, the apparent dip and apparent dip azimuth, corresponding to the largest measurement of semblance/similarity in the cell, are deemed to be estimates of the true dip and true dip azimuth of the traces therein. A color map, characterized by hue, saturation and lightness, is used to depict semblance/similarity, true dip azimuth and true dip of each cell; true dip azimuth is mapped onto the hue scale, true dip is mapped onto the saturation scale, and the largest measurement of semblance/similarity is mapped onto the lightness scale of the color map.

CROSS-REFERENCE

This patent application is a continuation in part of a provisionalpatent application filed Oct. 6, 1995, and having a Ser. No. 60/005,032and a U.S. patent application to Bahorich and Farmer, having a Ser. No.08/353,934 and a filing date of Dec. 12, 1994, now U.S. Pat. No.5,563,949.

TECHNICAL FIELD

This invention relates to the general subject of seismic explorationand, in particular, to methods and devices for identifying structuraland stratigraphic features in three dimensions.

BACKGROUND OF THE INVENTION

In seismic exploration, seismic data is acquired along lines (see lines10 and 11 of FIG. 1) that consist of geophone arrays onshore orhydrophone streamer traverses offshore. Geophones and hydrophones act assensors to receive energy that is transmitted into the ground andreflected back to the surface from subsurface rock interfaces. Energy isoften provided onshore by Vibroseis® vehicles which transmit pulses byshaking the ground at pre-determined intervals and frequencies on thesurface. Offshore, airgun sources are usually often used. Subtle changesin the energy returned to surface often reflect variations in thestratigraphic, structural and fluid contents of the reservoirs.

In performing three-dimensional (3D) seismic exploration, the principleis similar; however, lines and arrays are more closely spaced to providemore detailed subsurface coverage. With this high density coverage,extremely large volumes of digital data need to be recorded, stored andprocessed before final interpretation can be made. Processing requiresextensive computer resources and complex software to enhance the signalreceived from the subsurface and to mute accompanying noise which masksthe signal.

After the data is processed, geophysical personnel assemble andinterpret the 3D seismic information in the form of a 3D data cube (SeeFIG. 2) which effectively represents a display of subsurface features.Using this data cube, information can be displayed in various forms.Horizontal time slice maps can be made at selected depths (See FIG. 3).Using a computer workstation, an interpreter can also slice through thefield to investigate reservoir issues at different seismic horizons.Vertical slices or cross-sections can also be made in any directionusing seismic or well data. Seismic picks of reflectors can becontoured, thereby generating a time horizon map. Time horizon maps canbe converted to depth to provide a true scale structural interpretationat a specific level.

Seismic data has been traditionally acquired and processed for thepurpose of imaging seismic reflections for structural and stratigraphicinterpretation. However, changes in stratigraphy are often difficult todetect on traditional seismic displays due to the limited amount ofinformation that stratigraphic features present in a cross-section view.While working with both time slices and cross-sections provides anopportunity to see a much larger portion of faults, it is difficult toidentify fault surfaces within a 3D volume where no fault reflectionshave been recorded.

Coherence is one measure of seismic trace similarity or dissimilarity.The more two seismic traces increase in coherence, the more they arealike. Assigning a coherence measure on a scale from zero to one, “0”indicates the greatest lack of similarity, while a value of “1”indicates total or complete similarity (i.e., two identical, perhapstime-shifted, traces). Coherence for more than two traces may be definedin a similar way.

One method for computing coherence was disclosed in U.S. Pat. No.5,563,949 to Bahorich and Farmer (assigned to Amoco Corporation) havinga Ser. No. 353,934 and a filing date of Dec. 12, 1994. Unlike the shadedrelief methods that allow 3D visualization of faults, channels, slumps,and other sedimentary features from picked horizons, the coherencyprocess devised by Bahorich and Farmer operates on the seismic dataitself. When there is a sufficient change in acoustic impedance, the 3Dseismic coherency cube developed by Bahorich and Farmer can be extremelyeffective in delineating seismic faults. It is also quite effective inhighlighting subtle changes in stratigraphy (e.g., 3D images ofmeandering distributary channels, point bars, canyons, slumps and tidaldrainage patterns).

Although the process invented by Bahorich and Farmer has been verysuccessful, it has some limitations. An inherent assumption of theBahorich invention is the assumption of zero mean seismic signals. Thisis approximately true when the correlation window exceeds the length ofa seismic wavelet. For seismic data containing a 10 Hz component ofenergy, this requires a rather long 100 ms window which can mixstratigraphy associated with both deeper and shallower time horizons.Shortening the window (e.g., to 32 ms results in higher verticalresolution, but often at the expense of increased artifacts due to theseismic wavelet. Unfortunately, a more rigorous, non-zero mean runningwindow cross correlation process is an order of magnitude morecomputationally expensive. Moreover, if seismic data is contaminated bycoherent noise, estimates of apparent dip using only two traces will berelatively noisy.

Thus, there is a need for methods and apparatus that would overcome theshortcomings of the prior art. In particular, improved resolution andcomputational speed are desirable. In addition, it would be highlydesirable to improve estimates of dip in the presence of coherent noise.

SUMMARY OF THE INVENTION

In accordance with the present invention, a method and an article ofmanufacture is disclosed for locating subterranean features, faults, andcontours. In one embodiment of the invention, the method comprises thesteps of: accessing 3D seismic data covering a pre-determined volume ofthe earth; dividing the volume into an array of relatively smallthree-dimensional cells, wherein each of said cells is characterized byat least five laterally separated and generally vertical seismic traceslocated therein; determining in each cell the semblance/similarity ofthe traces relative to two predetermined directions; and displaying thesemblance/similarity of each cell in the form a two-dimensional map. Inone embodiment, semblance/similarity is a function of time, the numberof seismic traces within the cell, and the apparent dip and apparent dipazimuth of the traces within the cell; the semblance/similarity of acell is determined by making a plurality of measurements of thesemblance/similarity of the traces within the cell and selecting thelargest of the measurements. In addition, the apparent dip and apparentdip azimuth, corresponding to the largest measurement ofsemblance/similarity in the cell, are deemed to be estimates of the truedip and true dip azimuth of the traces therein. Finally, a color map,characterized by hue, saturation and lightness, is used to depictsemblance/similarity, true dip azimuth and true dip of each cell; inparticular, true dip azimuth is mapped onto the hue scale, true dip ismapped onto the saturation scale, and the largest measurement ofsemblance/similarity is mapped onto the lightness scale of the colormap.

In another embodiment of the invention, an article of manufacture isdisclosed that comprises a medium that is readable by a computer andthat carries instructions for the computer to perform a seismicexploration process. In one embodiment, the computer accesses 3D seismicdata covering a pre-determined volume of the earth and the mediuminstructs the computer to: divide the volume into an array of relativelysmall three-dimensional cells, wherein each cell is characterized by atleast five laterally separated and generally vertical seismic traceslocated therein; determine in each cell the semblance/similarity of thetraces relative to two pre-determined directions; and store thesemblance/similarity of each cell for display in the form atwo-dimensional map. In one embodiment, the instructions on the mediumdefine semblance/similarity as a function of time, the number of seismictraces within the cell, and the apparent dip and apparent dip azimuth ofthe traces within the cell; the semblance/similarity of a cell isdetermined by making a plurality of measurements of thesemblance/similarity of the traces within the cell and by selecting thelargest of the measurements. In addition, the apparent dip and apparentdip azimuth, corresponding to the largest measurement ofsemblance/similarity in the cell, are deemed to be estimates of the truedip and true dip azimuth of the traces therein. The computer comprisesmeans for producing a color display that is characterized by hue,saturation and lightness; and the medium has instructions to map truedip azimuth onto a hue scale, true dip onto a saturation scale, and thelargest measurement of semblance/similarity onto a lightness scale.

The process of the invention is particularly well suited forinterpreting fault planes within a 3D seismic volume and for detectingsubtle stratigraphic features in 3D. This is because seismic traces cutby a fault line generally have a different seismic character than traceson either side of the fault. Measuring multi-channel coherence or tracesimilarity along a time slice reveals lineaments of low coherence alongthese fault lines. Such measures can reveal critical subsurface detailsthat are not readily apparent on traditional seismic sections. Also bycalculating trace similarity along a series of time slices, these faultlineaments identify fault planes or surfaces.

The process of the invention presents a multitrace semblance method thatis generally more robust in noisy environments than a three trace crosscorrelation method for estimating seismic coherency. In addition, thesemblance process presented in this patent application provides:

higher vertical resolution for good quality data than that of a threetrace cross correlation measurement of seismic coherency;

the ability to map the 3D solid angle (dip/azimuth) of coherent events;

the ability to generalize the concept of complex “trace” attributes toone of complex “reflector” attributes; and

by combining these enhanced complex trace attributes with coherency andsolid angle, the basis of quantitative 3D seismic stratigraphy dataattributes that are amenable to geostatistical analysis methods.

Moreover, seismic coherency versus dip maps of picked horizons allowanalysis of:

the structural and stratigraphic framework before detailed pickingstarts;

structural and stratigraphic features of the entire data volume,including zones that are shallower, deeper, and adjacent to the primaryzone of interest;

subtle features that are not respresentable by picks on peaks andtroughs; and

features internal to the top and bottom of formation or sequenceboundary picks.

Coupled with coherency, data cubes of the solid angle dip of coherentseismic reflection events allow one to quickly see structural as well asstratigraphic relationships (such as onlap and offlap) between theseismic data and interpreted sequence boundaries.

Numerous other advantages and features of the present invention willbecome readily apparent from the following detailed description of theinvention, the embodiments described therein, from the claims, and fromthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings (s) will be provided by the Office upon request andpayment of the necessary fee.

FIG. 1 is a schematic diagram showing an arrangement of geophones toobtain 3D seismic data from the earth's subsurface for processing inaccordance with the present invention;

FIG. 2 is a pictorial representation of the information obtained fromthe data acquired using the arrangement of FIG. 1;

FIG. 3 is a pictorial representation of a horizontal time slice (t=1200ms) of 3D seismic data processed in accordance with the prior art;

FIGS. 4A through 4H illustrate various analysis windows (computationalstars) that may be used in running window analysis of seismic coherence,dip and dip azimuth;

FIG. 5 is a pictorial representation of the process of the inventionusing an elliptical window centered about an analysis point;

FIGS. 6A and 6B are examples of a rectangular dip/azimuth tessellationuseful when analyzing a survey having strikes and dips parallel to theacquisition axes, and when illuminating faults cutting perpendicular toa dominant reflector strike and dip (p_(O), q_(O));

FIGS. 7A through 7C are pictorial representations of three tesselationsof solid angle dip/azimuth space;

FIGS. 8A through 8D depict the mapping of 3D seismic attributes (φ,c,d)to 3D color space (H,L,S);

FIG. 9 shows four surfaces through the color hemisphere of FIG. 8A forfour values of coherence;

FIGS. 10A through 10C depict ordinary vertical slices of the seismicdata of FIG. 3;

FIGS. 11A through 11C depict the seismic attributes, dip, dip azimuthand coherency obtained by applying the process of the invention, to datacorresponding to that of FIGS. 10A through 10C;

FIGS. 12A and 12B are time slices (t=1200 ms and t=1600 ms) through thedip azimuth cube giving rise to FIGS. 11A and 11B;

FIGS. 13A and 13B are gray scale displays of coherency;

FIGS. 14A through 14C depict coherency slices corresponding to the dataof FIGS. 10A through 10C;

FIGS. 15A and 15B depict the results of applying a semblance algorithmand applying dip/azimuth algorithm in accordance with the presentinvention; and

FIGS. 16A and 16B are schematic diagrams depicting the processing flowof the steps performed in one embodiment of the invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings, and will herein be described indetail, specific embodiments of the invention. It should be understood,however, that the present disclosure is to be considered anexemplification of the principles of the invention and is not intendedto limit the invention to any specific embodiment or algorithm describedherein.

Before describing the invention in detail, an overview will be given sothat the detailed description, which follows, may be better understood.One embodiment of the process of the invention is illustrated in FIG.16A. Briefly, the method comprises the steps of: accessing 3D seismicdata 10 covering a pre-determined volume of the earth; dividing 12 thevolume into an array of relatively small three-dimensional cells,wherein each of said cells is characterized by at least five laterallyseparated and generally vertical seismic traces located therein;determining 14 in each cell the semblance/similarity of the tracesrelative to two pre-determined directions; selecting 16 the largest ofthe measurements; and displaying 24 the semblance/similarity of eachcell in the form a two-dimensional map. The semblance/similaritymeasurements may be recorded 18 for future use, or sent 20 to aninteractive workstation for further analysis; or printed or displayed asa color map 22, characterized by hue, saturation and lightness, may beused to depict semblance/similarity, true dip azimuth and true dip ofeach cell.

The first step of the process (See FIG. 16A) is to obtain a set ofseismic data in the form of seismic signal traces distributed over athree dimensional volume of the earth. Methods by which such data isobtained and reduced to digital form for processing as 3D seismic dataare known to those skilled in the art.

The Semblance Process

The next step is to generate a “coherence cube.” This is done byapplying a multi-trace semblance algorithm to the 3D seismic data. Thisalgorithm may take many forms. Whatever its form, its function is tocompare the similarity of nearby regions of seismic data within the 3Dseismic volume. This value (or attribute) serves as a rather robustestimate of signal discontinuity within geologic formations, as well assignal discontinuities across faults and erosional unconformities.

We define an analysis grid (or computational star) to be either anelliptical or rectangular pattern of “J” traces centered about a givenoutput trace (See FIGS. 4A through 4H).

In the drawings “X” denotes the center of the analysis window while “O”denotes additional traces used in the semblance calculation. Minimumsize circular and rectangular windows used to analyze data with equaltrace spacings (Δx=Δy) are shown in FIGS. 4A and 4D. Minimum circularand rectangular windows used to analyze data with trace spacing in thecross-line/strike (y) direction twice that in the in-line/dip (x)direction (Δy=2Δx) are shown in FIGS. 4B and 4E. Such nonequal spacingsare commonly used to exploit the slower change of geology in the strikedirection. Larger analysis windows used for greater resolution ofreflector dip and azimuth, or to increase signal to noise ratio in poordata areas, are shown in FIGS. 4C and 4F.

Elliptical and rectangular analysis windows centered about an analysispoint defined by a major axis, a minor axis, and the azimuth of majoraxis are shown in FIGS. 4G and 4H. The acquisition (x,y) axes arerotated by φ_(O) degrees from the North-East (x′, y′) axes. Suchassymmetric windows are useful in fracture detection.

If we center the (x, y) axis about the center of an analysis windowcontaining J seismic traces, u_(j)(t, x_(j), y_(y)), we define thesemblance σ(τ,p,q) to be: $\begin{matrix}{{\sigma \left( {\tau,\pi,q} \right)} = \frac{\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {{\tau - \left( {{px}_{j} + {qy}_{j}} \right)},x_{j},y_{j}} \right\rbrack}} \right\}^{2}}{J{\sum\limits_{j = 1}^{J}\quad \left\{ {u\left\lbrack {{\tau - \left( {{px}_{j} + {qy}_{j}} \right)},x_{j},y_{j}} \right\rbrack} \right\}^{2}}}} & (2)\end{matrix}$

where the triple (τ,p,q) defines a local planar event at time τ, and pand q are the apparent dips in the x and y directions measured in ms/m.Since, p=d sin φ and q=d cos φ, where d is the true dip and φ is the dipazimuth, it follows that:

u_(f)(τ,p,q,x,y)=u_(f)[τ−d(x sinφ+y cosφ), x, y].

Those skilled in the art will recognize that, in the denominator ofequation (1), J serves as a normalization factor. The numeratorrepresents the average energy and the summation term in the denominatorrepresents the total energy of the traces. In effect, equation (1) isrepresentative of a ratio of coherent and incoherent energy.

The objective is to perform a simultaneous 2D search (See FIG. 5) overapparent dips (p,q) in the in-line and cross-line directions. However,the semblance estimate given by equation (1) will be unstable for smallbut coherent values of seismic events, such as might occur if we were tosum along the zero crossings of a plane coherent wavelet. To avoid this,we estimate the coherence c (τ,p,q) at time τ and apparent dips (p,q) tobe the average semblance over a time window (or vertical analysis windowof height 2 w ms of half length K=w(Δt samples): $\begin{matrix}{{c\left( {\tau,p,q} \right)} = \frac{\sum\limits_{k = {- K}}^{+ K}\quad \left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {{\tau + {k\quad \Delta \quad t} - \left( {{px}_{j} + {qy}_{j}} \right)},x_{j},y_{j}} \right\rbrack}} \right\}^{2}}{J{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad \left\{ {u\left\lbrack {{\tau + {k\quad \Delta \quad t} - \left( {{px}_{j} + {qy}_{j}} \right)},x_{j},y_{j}} \right\rbrack} \right\}^{2}}}}} & (2)\end{matrix}$

In general, we do not know but wish to estimate that value of (p,q)associated with the local dip and azimuth of a hypothetical 3Dreflection event.

In one embodiment of the process of the invention, we estimate (p,q)through a brute force search over all possible apparent dips (See FIGS.6A and 6B). We assume that the interpreter is able to estimate themaximum true dip, d_(max) (measured in ms/m) from conventional seismicdisplays of the data (e.g., vertical data slices), thereby limiting thedips to be:

{square root over (p²+q²)}≦+d_(max).

If x_(max) and y_(max) are the half width and half length of arectangular analysis window, and if f_(max) is the highest temporalfrequency component contained in the seismic data, then the Nyquistcriterion of sampling the data at two points per period restricts theapparent dip increments, Δp to Δq, to:

x_(max)Δp≧1(2f_(max)), and y_(max)Δq≦1/(2f_(max)).

It should be noted that the Nyquist criterion is valid for linearoperations on the seismic data; and that equation (2) is nonlinear. Inpractice, we have found it necessary to limit Δp and Δq to half thatrequired by the Nyquist sampling criterion to obtain an accuratesemblance for a coherent dipping event.

Thus, our search for an estimate of the apparent dip ({circumflex over(p)}, {circumflex over (q)}) of a seismic reflector is reduced to thecalculation of semblance c(p_(l), q_(m)) over n_(p) * n_(q) discreteapparent dip pairs (p_(l), q_(m)) where:

n_(p)=(2d_(max)/Δp)+1, and

n_(q)=(2d_(max)/Δq)+1.

The apparent dip pair (p_(l), q_(m)) is deemed to be an estimate of thereflector apparent dips when:

c(p,q)≧c(p_(l), q_(m))   (3)

for all −n_(p)<1≦+n_(p), −n₁≦m≦+n₁.

The estimated apparent dips ({circumflex over (p)}, {circumflex over(q)}) are related to the estimated true dip d and dip azimuth{circumflex over (φ)} by the simple geometric relationships:p̂ = d̂sin φ̂;  and  q̂ = d̂cos φ̂,

where {circumflex over (d)} is measured in ms/m and the angle{circumflex over (φ)} is measured clockwise from the positive x′ (orNorth) axis. A simple coordinate rotation by angle φ_(O) is necessarywhen the in-line acquisition direction x is not aligned with the N-S(x′) axis (See FIG. 4G).

Solid Angle Discretization and Display

Optimal angular discretization is important for two reasons:minimization of computational cost, and limitation on the number ofcolors that can be displayed using commercial interpretation workstationsoftware (e.g., currently 64 with Landmark's “Seisworks” and 32 withGeoquest's “IESX” systems).

FIG. 7A shows the discretization of apparent dip using equal incrementsΔp and Δq in a rectangular grid of 69 angles. FIG. 7B shows thediscretization using equal increments Δd and Δφ in a radial grid of 97angles. Clearly, we do not wish to sample the dip d=0 ms/m for tendifferent azimuths. The “Chinese Checker” tessellation of FIG. 7C moreclosely represents an equal and therefore more economic sampling of the(d, φ) surface with a minimum number of points (i.e., 61 angles). Eachtesselation of FIGS. 7A and 7C represents an approximately equal patchof solid angle ΔΩ. For the angular discretization shown in FIG. 7C andfor a circular analysis radius, a, the incremental dip Δd is chosen tobe: $\begin{matrix}{{a\quad \Delta \quad d} \leq {\frac{1}{2\quad f_{\max}}.}} & (4)\end{matrix}$

Display

While it is possible to independently map semblance, dip, and azimuth,it is clear that the latter two attributes are coupled to each other.Furthermore, the confidence we have in these estimates is proportionalto the coherency/semblance. Others (See U.S. Pat. No. 4,970,699 toBucher et al. and assigned to Amoco Corporation. “Method for ColorMapping Geophysical Data”) have shown that the color HLS (hue,lightness, saturation) model can be quite effective in displayingmulticomponent seismic attributes (Also see Foley, J. D. and Van Dam,A., 1891, Fundamentals of Interactive Graphics, Addison-Wesley, Reading,Mass.).

Refering to FIGS. 8A through 8D, in this scheme, we directly mapazimuth, φ, onto the hue axis H:

H=φ

where both H (commonly known as the “color wheel”) and φ vary between−180 and +180 degrees (See FIG. 8B). Blue corresponds to North, salmonto East, yellow to South, and forest green to West azimuth. Azimuthscorresponding to zero dip are arbitrarily assigned a value of 0 degrees(North) and are thus plotted as blue.

Next, we map (See FIG. 8C) average semblance/coherence c, onto thelightness axis L:

L=αc,

where

0≦L≦100,

0≦c≦1.0, and

α is a scale constant less than 100, since changes in hue and saturationnear L=0 (black) and L=100 (white) are difficult to distinguish. White,or L=100, corresponds to high semblance or c=1, while black, or L=100,corresponds to low semblance, c=0. Intermediate semblances correspond tointermediate shades of gray, (such as silver, gray and charcoal gray).Lightness (sometimes referred to as “brightness”) expresses the amountof illumination. It represents a gray scale ranging from black to white.

Finally, we map dip d onto the saturation axis S:

S=100 d/d_(max)

The saturation (S=0) and hue chosen are arbitrary; we could just aseasily have displayed this attribute for a value of (H=0, S=100) givingus semblance displayed as white, pastel blue, pure blue, midnight blueand black. Saturation expresses the lack of dilution of a color by whitelight. A fully saturated color has no white added; adding white “washesout” the color without changing its hue. (See FIG. 8D).

FIG. 9 illustrates four constant surfaces through the 3D (H,L,S) colorhemisphere of (φc, d) shown in FIG. 8A, corresponding to c=100, c=0.75,c=0.50 and c=0.00.

Appendix 1 describes the color scheme in greater detail. Some advantagesof the HLS color model are: azimuth is cyclic and maps neatly to thecyclic color wheel (hue); the azimuths corresponding to d=0 aremeaningless; all azimuths converge smoothly to gray for shallow dips;and lower confidence in estimating dip and azimuth in zones of weak, lowsemblance (such as across faults) is indicated by darker colors.

Implementation of Mathematical Process

Landmark and GeoQuest interpretive workstations (See FIG. 16B), forexample, can be used to view and interpret faults and stratigraphicfeatures by loading the processed data as a seismic volume.Visualization software (e.g., Landmark's SeisCube software) may beemployed to rapidly slice through the seismic volume to aid inunderstanding complex fault relationships.

Computer Program

A FORTRAN 77 program was written to perform the calculations and providethe information for the displays previously described. Additionaldetails are given in Appendix 2. Each trace U_(MN) is accessed by itsin-line and cross-line indices, M and N. The user specifies arectangular or an elliptical spatial analysis window or cell about eachpoint/trace in the input data set (See FIG. 4G). The major and minoraxis of this analysis window, a and b are given by a=aplength andb=apwidth. The orientation or azimuth of the major axis φ_(a) is givenby φ_(a)=apazim. A rectangular analysis window (FIG. 4H) is indicated byspecifying −R on the command line. The 2J indices relative to the centerof this analysis window (and corresponding to the traces that fallwithin this window) are tabulated as a simple list, with m(j) and n(j)indicating the trace index (relative to the analysis trace U_(MN)) inthe x and y directions, respectively. The program performs asimultaneous 2D search over apparent dips (p,q) in the in-line andcross-line directions, where (p²+q²)^(1/2)<+smax. The increments dp anddq are chosen such that the data are sampled at four points perperiod<1/(fref) at the edge of the analysis window. For interpretation,it may be convenient to express each apparent dip pair (p,q) inspherical coordinates as a true (time or depth) dip d and dip azimuth φ.

The data in the analysis window are interpolated to the fractional time,τ−px−qy, for each trial dip and azimuth (See FIG. 5), in essence,“flattening” of data. The semblance for this trial dip at the analysispoint is defined to be the semblance of these flattened traces in theanalysis window.

For time domain data, we flatten the jth trace about the analysis point(M,N) by:

u_(f)(τ,p,q,x,y)=u[τ−(px+qy)]=u[τ−d(x sinφ+y cosφ)].

where x and y are distances measured from the center of the analysiswindow. This may be expressed

u^(f)_(M+m(j),N+n(j))(τ,p,q)=M_(M+m(j),N+n(j))[τ−(pn_((j))(j)Δx+qm(j)Δy)]

where Δx and Δy are the in-line and cross-line trace spacings.

For depth domain data we flatten the jth trace using:

u_(f)(ξ,p,q,x,y)=u[ξ−(px+qy)]=u[τ−d(x sinφy cosφ)].

The semblance is ;then calculated for all subsequent dips and azimuthsusing: $\begin{matrix}{{\sigma \left( {\tau,p,q} \right)} = \frac{\quad \left( {\sum\limits_{j = 1}^{J}\quad \left\lbrack {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)} \right\rbrack} \right)^{2}}{J{\sum\limits_{j = 1}^{J}\quad \left\lbrack {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)} \right\rbrack^{2}}}} & (5)\end{matrix}$

As in velocity analysis, the semblance for each dip, azimuth andanalysis point are smoothed by forming a running window time integrationover the partial sums from −K to +K where K=apheight/dt. We thereforedefine the coherence, c(τ,p,q) to be: $\begin{matrix}{{c\left( {\tau,p,q} \right)} = \frac{\quad {\sum\limits_{- K}^{+ K}\quad \left( {\sum\limits_{j - 1}^{J}\quad \left\lbrack {u_{f}\left( {t,p,q,x,y} \right)} \right\rbrack} \right)^{2}}}{J{\sum\limits_{- K}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad \left\lbrack {u_{f}\left( {t,p,q,x,y} \right)} \right\rbrack^{2}}}}} & (6)\end{matrix}$

That dip and azimuth pair Ω=(d, φ) which has the maximum (running windowintegrated) coherency c is taken to be an estimate of the coherency,{overscore (c)}, dip and azimuth ({circumflex over (d)}, {circumflexover (φ)}) for the analysis point.

EXAMPLES

FIGS. 11A through 11C are displays of the 3D seismic attributes (φ, c,d) corresponding to FIGS. 10A through 10C using the semblance basedcoherency algorithm expressed by equation (6), and the color displaytechnique depicted in FIGS. 8 and 9. The input data were temporarilysampled at 4 ms, have an in-line trace spacing of Δx=12.5 m, and have across-line trace spacing of Δy=25 m, with the in-line acquisitionoriented along a N-S axis. For FIGS. 11A through 11C, a circularanalysis window or cell of a=b=60 m was used (See FIG. 4A), so as toinclude a total of 11 traces in the calculation. The maximum search dip(See FIG. 7C) was d_(max)=0.25 ms/m, giving rise to 61 search angles.The temporal integration time used was w=16 ms, or K=4, therebyaveraging the semblance calculation over 9 samples.

In FIGS. 10A and 10B lines AA′ and BB′ were chosen as S to N and W to Evertical slices through the center of a salt dome. Line CC″ is an offsetS to N line and illustrates the appearance of radial faults on avertical slice. In FIGS. 11A through 11C, the interior of the salt domeis represented by dark colors, corresponding to an area of generally lowcoherency. Low areas of coherency correspond to the radial faults seenon line CC″. Coherent, flat dips are represented as light gray anddominate the section away from the salt dome, in particular line CC″.The blue color on the north side of the salt dome (seen on N-S line AA′)corresponds to sediments dipping steeply (D=D_(max)) to the North. Thesedips become progressively shallower away from the salt dome, and arethus displayed first as blue (saturation, S=100.0), cadet blue (S=0.75)and steel blue (S=0.50), before they flatten and are displayed as gray(S=0.0). The yellow color on the south side of the salt dome (seen online AA′) corresponds to sediments dipping steeply to the South. Thesalmon color on the East flank of the salt dome (shown on the E-W lineBB′) corresponds to sediments dipping steeply to the East. These dipsalso become progressively shallow away from the salt dome, and aredisplayed first as salmon (S=100.00), through sienna (S=50.0), andfinally to gray, corresponding to flat dip. Finally, the forest greencolor on the West flank of the salt dome (shown on line AA′) correspondsto sediments dipping steeply to the West. These dips also flatten awayfrom the salt dome and are displayed using the colors shown on the Westpart of the legend shown in FIG. 9. N-S line CC′ is not aligned radiallywith the salt dome. Thus, out-of-the-plane rotation of different faultblocks are depicted, with the green block corresponding to dips to theSW and the cyan block with dips to the NW.

Since these 3D attributes were calculated for every point on the inputseismic volume, they can be displayed as horizontal attribute timeslices (See FIGS. 12A and 12B); these correspond to a time slice of theunprocessed seismic data. The interior of the salt dome, as well as theradial faults are displayed as dark colors, corresponding to incoherentzones of the data. Because of the nearly radial symmetry of the saltdiapir at t=1,200 ms (See FIG. 12A), the dipping sediments that flankthe diapir also radiate outward in an azimuthally simple fashion suchthat their azimuths correspond quite closely to the color legend on theleft side of FIG. 9. This pattern is somewhat less symmetric at t=1,600ms (See FIG. 12B), where there are shallower dips to the South than tothe North. In addition, internal blocks of coherent data can be seenwithin the salt dome.

The color legend displayed in FIG. 9 allows for only four “buckets” ofcoherency. In order to examine the coherency in greater detail, it canbe plotted as a single attribute. This is shown in FIGS. 13A and 13Bwhere all 184 colors are applied to the simple gray scale shown of FIG.8C. In this display, maximum coherency (c=1.0) is rendered as white;minimum coherency (c=0.0) is rendered as black. While the interior ofthe salt diapir is shown as a highly incoherent zone, this displaybetter shows subtle details in the radial faults patterns. Inparticular, faults emanating from the salt dome are shown, with somebifurcating as we move away. In addition to more continuous binning ofthe coherency attribute, part of this difference in perception is due tothe fact that the human retina sees colors and black and white usingdifferent (cone vs. rod) receptors. There is also a physiologicaldifference in the ability to differentiate between greens and bluesbetween male and female populations. For this reason, male interpretersoften prefer the simple single attribute coherency display shown inFIGS. 13A, 13B and FIG. 15A over the multiattribute (φ,c,d) displayshown in FIGS. 11A through 12B and FIG. 15B. In actuality, thesedisplays are quite complimentary: the 3D component display being usefulin recognizing the appearance of conflicting dips azimuths betweenadjacent rotated fault blocks; and the single component display beingused to enhance the edge, or incoherent fault discontinuity, separatingthem.

Process Considerations

Careful study of FIGS. 13A and 13B reveals a ring-like pattern ofincoherent energy circumscribing the salt dome. To investigate the causeof these artifacts, vertical slices were taken through the singlecomponent coherency cube corresponding to the seismic data in FIGS. 10Athrough 10C. This is shown in FIGS. 14A through 14C. The interior of thesalt dome is clearly incoherent. An incoherent submarine canyon feature(described by Nissen et al., “3D Seismic Coherency Techniques Applied tothe Identification and Delineation of Slump Features”, 1995 SEG ExpandedAbstracts, pages 1532-1534) is shown to the north of the salt dome. Ifthe seismic data shown in FIGS. 10A through 10C were overlayed on thecoherency section shown in FIGS. 14A through 14C, one would see a closecorrespondence between areas of low coherency of FIGS. 14A through 14Cwith zero crossings of the seismic reflection events in FIGS. 10Athrough 10C. This is easily understood if it is assumed that there is afixed, but incoherent, level of seismic noise throughout the data. Foranalysis points where the apparent dips are aligned with the peaks ortroughs of strong amplitude seismic reflectors (such that the estimateof signal energy is high with respect to the incoherent noise), one canexpect the signal-to-noise ratio to be high, giving rise to an estimateof high coherency. However, if, our analysis point is such that thereare apparent dips aligned with the zero crossings of these same seismicreflectors, such that the signal is low with respect to our incoherentnoise, one can expect the signal-to-noise ratio to be low, giving riseto a low estimate of coherency.

We have found three methods for increasing the signal-to-noise ratio:the first more appropriate for structural analysis; the second moreappropriate for stratigraphic analysis, and the third appropriate forboth.

For the case of steeply dipping (less than 45 degrees from the vertical)faults, the signal-to-noise ratio can be increased by simply increasingthe size of our vertical analysis window w given in equation (2). Twoeffects will be observed. First, the structural leakage corresponding tothe zero crossing points of the reflectors diminishes as verticalintegration window size increases. Second, since few of the faults aretruly vertical, the lateral resolution of the faults appears to decreaseas the vertical window size increases. An analysis window of w=16 ms(which would encompass a full cycle of the peak 30 Hz energy in thedata) appears to be in good compromise.

The second method (equally appropriate for stratigraphic and structuralanalysis) of increasing the signal-to-noise ratio, is to extractcoherency along an interpreted stratigraphic horizon. If thisstratigraphic horizon is associated with an extremum of the seismicdata, such as a peak or trough, those data having only a relatively highsignal-to-noise ratio are selectively displayed. Clearly, extractingcoherency data corresponding to a zero crossing would greatly exacerbatethe coherency display. A more economic version of this approach is tofirst flatten the data along the horizon of interest and then calculatethe seismic attributes only along the picked horizon. This approach issomewhat more sensitive to busts in automatic (and human!) pickers,since cycle skip glitches in the picking are somewhat random andtherefore will almost always appear as incoherent.

Shallow features (e.g., shallow channels; shallow tidal channel featurescorresponding to reworked deltaic sands; and small en echelon faulting)do not exist for any distance above or below an interpretedstratigraphic horizon; therefore, the inclusion of any data from aboveor below the horizon in which they are located adds uncorrelatedamplitude variations, thereby making these discontinuities look morecoherent, and hence washed out. If the time samples above or below theinterpreted horizon contain independent, perhaps strong amplitudediscontinuities, these discontinuities will bleed into the analysis forlarge windows, giving a stratigraphic horizon containing features mixedfrom stratigraphic different horizons generated at different geologictimes.

The third method is a generalization of the original collection ofseismic traces u_(j) to that of an analytic trace v_(j) defined as:

v_(j)(t)≡v u_(j)(t)+iu_(j) ^(H)(t)

where u_(j) ^(H)(t) is the quadrature, or Hilbert transform of u_(j)(t),and i denotes {square root over (−1)}. The calculation of σ(τ,p,q) andc(τ,p,q) is entirely analogous to equations (1) and (2), where we notethat the definition of v_(j) ² is given by

v_(j) ²≡v_(j)v_(j)*≡(u_(j)+iv_(j) ^(H)iu_(j) ^(H))(u_(j)−iv_(j)^(H)iu_(j) ^(H)).

The third method avoids numerical instabilities in the semblanceestimate of equation (1) at the “zero-crossings” of an otherwise strongreflector.

The Effect of the Horizontal Analysis Window

By examining equation (2), it is clear that the computational cost ofanalysis increases linearly with the number of traces included in theanalysis. However, by comparing a semblance based 11-trace coherencytime slice with those of a 3-trace cross correlation coherency timeslice, (where each has an identical vertical analysis window of w=32 ms)one is led to believe that adding more traces to the computation canincrease the signal-to-noise ratio. In general, the signal to noiseratio increases as we increase the size of the analysis window. However,the overall coherency decreases somewhat (one sees less white), sincethe approximation of a possibly curving reflector by a constant (p,q)planar event breaks down as we increase the window size. In general, thesignal-to-noise ratio of dip/azimuth estimates increases with the numberof traces in the calculation, until a point is reached whereby thelocally planar reflector approximation no longer holds.

Conclusions

The 3D semblance technique presented in this patent application providesan excellent measurement of seismic coherency. By using an arbitrarysize analysis window, we are able to balance the conflictingrequirements of maximizing lateral resolution and signal-to-noise ratiothat is not possible when using a fixed three trace cross correlationtechnique. Accurate measurements of coherency can be achieved by using ashort temporal (vertical) integration window that is on the order of theshortest period in the data, whereas a zero mean cross correlationtechnique preferably is used with an integration window that is greaterthan the longest period in the data. Thus, the semblance process resultsin less vertical smearing of geology than a cross correlation process,even for large spatial analysis windows (See FIGS. 15A and 15B). Equallyimportant to the coherence estimate, the semblance process provides adirect means of estimating the 3D solid angle (dip and azimuth) of eachreflector event. These solid angle maps may or may not be related toconventional time structure maps defining formation boundaries. Like thebasic coherency process of Bahorich and Farmer (e.g., crosscorrelation), estimation of the instantaneous dip/azimuth cube can beachieved prior to any interpretation of the data for use in a grossoverview of the geologic setting. In this reconnaissance mode, thecoherency and instantaneous dip/azimuth cubes allow the user to pick keydip and strike lines crossing important structural or sedimentologicfeatures very early in the interpretation phase of a project. In aninterpretation mode, these dips and azimuths may be related to formationand/or sequence boundaries, such that one can map progradation andtransgression patterns of the internal structure in 3D. Finally, havingestimated the instantaneous dip and azimuth at every point in the datacube, one can apply conventional seismic trace attributes to locallyplanar reflectors, thereby greatly increasing signal-to-noise ratios.

From the foregoing description, it will be observed that numerousvariations, alternatives and modifications will be apparent to thoseskilled in the art. Accordingly, this description is to be construed asillustrative only and is for the purpose of teaching those skilled inthe art the manner of carrying out the invention. Other algorithms maybe used to measure the similarity of nearby regions of seismic data orto generate the “discontinuity cube.” Moreover, equivalent computationsmay be substituted for those illustrated and described. For example,instead of a search over apparant dips p and q, one could search overdip and azimuth (d, φ). The inverse of the computed semblance may beused so as to obtain a display analogous to the negative of aphotograph. Also certain features of the invention may be usedindependently of other features of the invention. For example, after thesolid angle (dip and azimuth) has been estimated, a smoother and morerobust multitrace estimate of the conventional complex trace attributes(Taner, M. T., Koehler, F., and Sheriff, R. E.; 1979; “Complex SeismicTrace Analysis;” Geophysics, 44, 1041-1063) may be obtained. Instead ofcalculating these attributes on a single trace, one can calculateattributes of the angle stack of traces within the analysis window. Thatis, one can calculate:${{a_{i}\left( {\tau,p,q} \right)} = \left\{ {\left\lbrack {U\left( {\tau,p,q} \right)} \right\rbrack^{2} + \left\lbrack {U^{H}\left( {\tau,p,q} \right)} \right\rbrack^{2}} \right\}^{1/2}},{{\Psi_{i}\left( {\tau,p,q} \right)} = {\tan^{- 1}\left\{ {{U^{H}\left( {\tau,p,q} \right)}/{U\left( {\tau,p,q} \right)}} \right\}}},{f_{i} = {\frac{\quad \psi}{\tau} = \frac{{{U\left( {\tau,p,q} \right)}\frac{\partial U^{H}}{\partial\tau}\left( {\tau,p,q} \right)} + {{U^{H}\left( {\tau,p,q} \right)}\frac{\partial U}{\partial\tau}\left( {\tau,p,q} \right)}}{\left\lbrack {U\left( {\tau,p,q} \right)} \right\rbrack^{2} + \left\lbrack {U^{H}\left( {\tau,p,q} \right)} \right\rbrack^{2}}}}$

and${b_{j}\left( {\tau,p,q} \right)} = \frac{{{{U\left( {\tau,p,q} \right)}\frac{\partial U}{\partial\tau}\left( {\tau,p,q} \right)} + {{U^{H}\left( {\tau,p,q} \right)}\frac{\partial U^{H}}{\partial\tau}\left( {\tau,p,q} \right)}}}{\left\lbrack {U\left( {\tau,p,q} \right)} \right\rbrack^{2} + \left\lbrack {U^{H}\left( {\tau,p,q} \right)} \right\rbrack^{2}}$

where U(τ, p, q)  is$\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left( {{\tau - \left( {{px}_{j} + {qy}_{j}} \right)},x_{j},y_{j}} \right\rbrack}} \right\}^{2}$

(See the numerator of equation 1);

U^(H)(τ,p,q) is the Hilbert transform, or quadrature component of U(τ,p, q);

a_(i)(τ,p,q) is the envelope, or instantaneous amplitude;

Ψ_(i)(τ,p,q) is the instantaneous phase;

f_(i)(τ,p,q) is the instantaneous frequency; and

b_(i)(τ,p,q) is the instantaneous bandwidth (See Cohen, L.; 1993;“Instantaneous Anything;” Proc. IEEE Int. Conf. Acoust. Speech SignalProcessing, 4, 105-109).

In addition to these “instantaneous” attributes, other attributes aresuggested to characterize the signal within a given lobe of the traceenvelope to be that of the attribute at the peak of the envelope τ_(θ).These include (See Bodine, J. H.; 1994; “Waveform Analysis with SeismicAttributes;” presented in the 54th Ann. Intl. Mtg. SEG. Atlanta, Ga.,USA):

the wavelet envelope:

a_(r)(τ,p,q)=a_(i)(τ_(θ),p,q),

the wavelet phase:

Ψ_(r)(τ,p,q)=Ψ_(i)(τ_(θ),p,q),

the wavelet frequency:

f_(r)(τ,p,q)=f_(j)(τ_(θ),p,q),

the wavelet bandwidth:

b_(r)(τ,p,q)=b_(i)(τ_(θ),p,q),

the zero phase component:

U⁰(τ,p,q)=cos[Ψ_(r)(τ,p,q)]U(τ,p,q)+sin[Ψ_(r)(τ,p,q)]U^(H)(τ,p,q)

the ninety degree phase component:

U⁹⁰(τ,p,q)=−sin[Ψ_(r)(τ,p,q)]U(τ,p,q)+cos[Ψ_(r)(τ,p,q)]U^(H)(τ,p,q)

as well as skewness, rise time, and response length. Since mixing occursalong the true dip direction, slowly varying amplitude, phase,frequency, and bandwidth components of the event will be preserved.Moreover, the computation of coherency/semblance/similarity allows oneto perform “texture analysis” of similar seismic regions. Textureanalysis combined with “cluster analysis” leads to segmentationanalysis. Among other things, this allows one to make geologiccorrelations and extrapolate the geological character of the subsurface.In addition, determination of the coherency may be used to impose apriori constraints for both post-stack and pre-stack seismic inversion.Thus, it will be appreciated that various modifications, alternatives,variations, and changes may be made without departing from the spiritand scope of the invention as defined in the appended claims. It is, ofcourse, intended to cover by the appended claims all such modificationsinvolved within the scope of the claims.

APPENDIX 1 MULTIATTRIBUTE HLS CALIBRATION direction φ (hue) CrayolaColor The hues are pure, or 100% saturated colors, and correspond to thefollowing 1994 non-toxic 96 crayon “Crayola” standard: N 0 blue NNE 30plum ENE 60 magenta E 90 salmon ESE 120 red SSE 150 orange-red S 180yellow SSW 210 lime-green WSW 240 green W 270 forest-green WNW 300 cyanNNW 330 cerulean N 360 blue Partial 50% saturation corresponds to“dirtier” or “muddier” colors: N 0 cadet blue NE 45 fuscia E 90 maroonSE 135 sepia S 180 gold SW 225 olive W 270 sea green NW 315 steel blue N360 cadet blue 0% saturation corresponds to no color pigment: N 0 gray E90 gray S 180 gray W 270 gray N 360 gray Low values of lightnesscorrespond to “dark” colors; intermediate values of lightness correspondto “deep” colors, and high values of lightness correspond to “pastel”colors.

APPENDIX 2 SYNOPSIS \semb3d [-Nfile_in] [-Ofile_out] [-hisfile_his][-tstarttstart] [-tendtend] [-ildmdx] [-cldmdy] [-aplengthaplength][-apwidthapwidth] [-apheightapheight] [-apazimapazim] [-llazlmxazim][-clazimyazim] [-dzdz] [-smaxsmax] [-pminpmin] [-pmaxpmax] [-qminqmin][-qmaxqmax] [-threshthresh] [-freffref] [-startlinestartline][-endlineendline] [-exppower] [-min] [-int] [-R] DESCRIPTION semb3dreads in 3D seismic post stack time or depth data and generatessemblance, dip and azimuth outputs. COMMAND LINE ARGUMENTS semb3d getsall its parameters from command line arguments. These arguments specifythe input, output, spatial analysis window, and dip discretizationparameters. The following command line arguments have been used in oneembodiment of the invention. -Nfile_ in Enter the input data set name orfile immediately after typing -N. This input file should include thecomplete path name if the file resides in a different directory.Example: -N/export/data2/san_ juan/time_ stack tells the program to lookfor file ‘time_ stack’ in directory ‘/export/data2/san_ juan’. For thisprogram, the data is stored as a rectangular grid of regularly binneddata. The number of traces (denoted by lineheader word ‘NumTrc’) definesthe number of traces in the ‘x’ direction. The number of records(seismic lines denoted by lineheader word ‘NumRec’) defines the numberof traces in the ‘y’ direction. Missing data padded in with dead tracesflagged by a dead trace header flag. -Ofile_ out Enter the outputmulti-attribute data set name or file immediately after typing -O.Attributes will be output back to back, line by line. Without scalingthe semblance c will range between 0.0 and 1.0. The values of dip willrange between 0 and smax and will always be positive (pointing down).Units are in msec/m (msec/ft) for time data, or m/m (ft/ft) for depthdata. The azimuth φ is perpendicular to strike and points in thedirection of maximum positive dip (pointing down). The values of azimuthwill range between 0 and 360 degrees. Properly defined, an outputazimuth of 0. degrees corresponds to North, while an output azimuth of90 degrees corresponds to East. The values of OMEGA = (d, φ) can bechosen such that (when converted to an 8 bit integer) the left most 6bits correspond to a valid Seisworks color table. This color tablecorresponds to the HLS color model previously described and is generatedusing a program that maps the angles scanned into an HLS (hue,lightness, saturation) color map of OMEGA = (d, φ). -hls file_ hls Enter-hls followed by the hls table file name to output an ascii flat filecontaining the hue, lightness and saturation of each sample contained inthe output. This file is input to a program to generate a RGB (red,green, blue) color lookup table needed for a proper display on certainworkstations. -tstarttstart Enter -tstart followed by the beginning ofthe analysis window in msec. -tendtend Enter -tend followed by the endof the analysis window in msec. The output record will be (tend −tstart) msec long. -ildmdx After -ildm enter the in-line distancemeasure (trace separation) in m (ft). -cldmdy After -cldm enter thecross-line distance measure (line separation) in m (ft). -dzdz After -dzenter the vertical depth sample increment in m (ft). A value of dz >0indicates the data are in depth. -aplengthaplength After -aplength enterthe half aperture length (in meters or feet) along the azimuth of theelliptical analysis window to be used. Increasing the analysis window byincreasing aplength, apwidth will result in: (1) increased angularresolution, (2) decreased spatial resolution, (3) increasedcomputational cost; and (4) decreased overall coherency (since the planewave approximation is less valid. -apwidthapwidth After -apwidth enterthe half half aperture width (in meters or feet) perpendicular to theazimuth of the elliptical analysis window to be used. -apheightapheightAfter -apheight enter the half length in milliseconds (or meters orfeet) of the running time (depth) integration window applied over thesemblance. Example = ±2 samples. Increasing the temporal integrationwindow apheight will result in: (1) a smoothed, less noisy response, (2)decreased vertical resolution, and (3) no change in computational cost.-apazimapazim After -apazim enter the azimuth of the elliptical analysiswindow (with 0 being North and 90 being East). -smaxsmax After -smaxenter the maximum dip to be tested in msec/m (msec/ft) for time data, orin m/m (ft/ft) for depth data. This is recommended when there is nopreferential strike direction in the data. This value can be readdirectly from a section display of the data. smax will be on the orderof .30 msec/m (10 msec/ft) for time data. Increasing the value of smaxbeyond any true dips results in significantly increased computationalcost for an identical result. -pminpmin After -pmin enter the minimuminline (increasing trace number) dip to be tested in msec/m (msec/ft)for time data, or in m/m (ft/ft) for depth data. This is recommendedwhen there is a predominant strike direction parallel or perpendicularto the data acquisition lines. This value can be read directly from asection display of the data. -pmaxpmax After -pmax enter the maximumin-line (increasing trace number) dip to be tested in msec/m (msec/ft)for time data, or in m/m (ft/ft) for depth data. This is recommendedwhen there is a predominant strike direction parallel or perpendicularto the data acquisition lines. This value can be read directly from asection display of the data. Enter this command line argument to definea rectangular (2*aplength by 2*apwidth) vs. elliptical analysis windoworiented along the azimuth axis. -qminqmin After -qmin enter the minimumcross-line (increasing line number) dip to be tested in msec/m (msec/ft)for time data, or in m/m (ft/ft) for depth data. This is recommendedwhen there is a predominant strike direction parallel or perpendicularto the data acquisition lines. This value can be read directly from asection display of the data. -qmaxqmax After -qmax enter the maximumcross-line (increasing line number) dip to be tested in msec/m (msec/ft)for time data, or in m/m (ft/ft) for depth data. This is recommendedwhen there is a predominant strike direction parallel or perpendicularto the data acquisition lines. This value can be read directly from asection display of the data -threshthresh After -thresh enter thethreshhold or cutoff semblance value, below which dip and azimuth areconsidered to be valid measures; below this value shades of gray will bedisplayed. Some display software limits the number of colors availablefor display. -freffref After -fref enter the reference frequency incycles/sec (Hz) for time data, or in cycles/km (cycles/kft) used indetermining the number of dips to be searched (e.g., fref = 60 Hz fortime data, 30 cycles/km for depth data). -ilazimilazim After -ilazimenter the in-line azimuth (0 degrees being North, 90 degrees being East)that is the azimuth of increasing trace number. This value is used tocalibrate a solid angle output file, if used. -clazimclazim After-clazim enter the cross-line azimuth (0 degrees being North, 90 degreesbeing East) that is the azimuth of increasing line numbers. This valueis used to calibrate the solid angle output file, if used. -exppowerAfter -exp enter the exponent to be applied for non-linear scaling ofthe semblance. In general, most semblance/coherency values will bebetween 0.8 and 1.0. Scaling with power = 2.0 would map these valuesbetween .64 and 1.0, scaling with power = 4.0 would map these valuesbetween .41 and 1.0, and so forth. This is useful for loading data to aninterpretive workstation. -startlinestartline After -startline enter thefirst output line to be generated. -endlineendline After -endline enterthe last output line to be generated. -min After -min enter this commandline argument to extract the dip, azimuth, and semblance correspondingto the minimum semblance of the angles searched. (As a default, theprogram searches for the maximum semblance or coherency). -int Enterthis command line argument to scale output such that it can berepresented by an 8 bit integer ranging between −128 and +127. Usefulfor loading data to an interpretive workstation.

We claim:
 1. A method for the exploration of hydrocarbons, comprisingthe steps of: (a) obtaining a representation of a set of seismic tracesdistributed over a pre-determined three-dimensional volume of the earth,said volume of the earth having subterranean features characterized bydip and dip azimuth that are defined relative to a pre-defined dipazimuth measurement axis; (b) dividing said three-dimensional volumeinto at least one horizontal time layer, and dividing said time layerinto a plurality of three-dimensional analysis cells, wherein eachanalysis cell has two pre-determined, mutually perpendicular lateraldimensions and has portions of at least five laterally separated seismictraces located therein; (c) calculating, within each of said analysiscells, a plurality of measures of the semblance of said traces locatedtherein, wherein each measure of semblance is at least a function oftime, the number of seismic traces within said analysis cell, and theapparent dip and apparent dip azimuth of said traces within saidanalysis cell; (d) identifying, within each analysis cell, the largestof said calculated measures of semblance and defining the correspondingapparent dip and apparent dip azimuth to be an estimate of the true dipand an estimate of the true dip azimuth of the seismic traces withinsaid analysis cell; and (e) forming, from all of said analysis cells, aseismic attribute display from said largest calculated measures ofsemblance and said corresponding estimates of the true dip and the truedip azimuth of the seismic traces within said time layer.
 2. The methodof claim 1, where step (e) is performed by forming a color map that ischaracterized by hue, saturation and lightness, wherein one of saidestimates of true dip azimuth, said estimates of true dip, and saidlargest calculated measures of semblance is mapped onto one of alightness scale, hue scale, and a saturation scale; wherein another ofsaid estimates of true dip azimuth, said estimates of true dip, and saidlargest calculated measures of semblance is mapped onto another of saidlightness scale, said hue scale, and said saturation scale; and whereinthe remaining one of said estimates of true dip azimuth, said estimatesof true dip, and said largest calculated measures of semblance is mappedonto the remaining one of said lightness scale, said hue scale, and saidsaturation scale.
 3. The method of claim 2, where step (e) is performedby mapping said estimates of true dip azimuth onto said hue scale. 4.The method of claim 2, where step (e) is performed by mapping saidestimates of true dip onto said saturation scale.
 5. The method of claim2, where step (e) is performed by mapping said largest calculatedmeasures of semblance onto a lightness scale.
 6. The method of claim 1,where in performing step (c) each measure of semblance is at least afunction of the energy of said traces; and wherein said energy of saidtraces is a function of time, the number of seismic traces within saidanalysis cell, and the apparent dip and apparent dip azimuth of saidtraces within said analysis cell.
 7. The method of claim 6, wherein eachmeasure of semblance is at least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}$

and$\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances measured from the center of theanalysis cell, where p and q are the apparent dips in the x and ydirections respectively, and where u_(f)(t, p,q,x_(j)[x],y_(j)[y]) is aseismic trace within the analysis cell; and wherein the true dip d anddip azimuth φ are related to p and q by p=d sin φ [p] and q=d cos φ. 8.The method of claim 7, wherein each measure of semblance is a functionof:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}}.$


9. The method of claim 7, wherein each measure of semblance for eachdip, dip azimuth, and analysis point are smoothed by performing arunning window time integration over the partial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad \left\lbrack {u_{f}\left( {{t + {k\quad \Delta \quad t}},p,q,x_{j},y_{j}} \right)} \right\rbrack} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad \left\lbrack {u_{f}\left( {{t + {k\quad \Delta \quad t}},p,q,x_{j},y_{j}} \right)} \right\rbrack^{2}}}$

where K is the half width of the time window in samples.
 10. The methodof claim 1, wherein said traces within said analysis cells arecharacterized by a maximum dip and a maximum temporal frequencycomponent; and wherein step (c) includes the steps of: obtaining anestimate of the maximum true dip and the maximum temporal frequencycomponent of said traces in said analysis cell; using said maximum truedip, said maximum temporal frequency and said pre-determined lateraldimensions of said analysis cell to calculate apparent dip increments intwo generally perpendicular directions relative to said dip azimuthmeasurement axis.
 11. The method of claim 1, where in performing step(c) said measure is at least a function of:$\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}} \right\}^{2}$

where J is the number of traces in said analysis cell, whereu_(j)(τ,p,q) is a representation of the seismic trace in said analysiscell, where ρ is the time, p is the apparent dip in the x direction, andq is the apparent dip in the y direction; wherein p and q are measuredin ms/m and the x and y directions are mutually perpendicular.
 12. Themethod of claim 11, where in performing step (c) said measure is also afunction of$\sum\limits_{j = 1}^{J}\quad {\left\{ {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack} \right\}^{2}.}$


13. The method of claim 12, where in performing step (c) said measure isa function of:$\frac{\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}} \right\}^{2}}{\sum\limits_{j = 1}^{J}\quad \left\{ {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack} \right\}^{2}}.$


14. A method of locating subterranean features, faults, and contours,comprising the steps of: (a) accessing 3D seismic data covering apre-determined volume of the earth; (b) dividing said volume into anarray of relatively small three-dimensional cells wherein each of saidcells is characterized by at least five laterally separated andgenerally vertical seismic traces located therein; (c) determining ineach of said cells the semblance/similarity of said traces relative totwo pre-determined directions; and (d) recording saidsemblance/similarity of said cells in a form for display as atwo-dimensional map of subterranean features.
 15. The method of claim14, where in performing step (c) said pre-determined directions aremutually perpendicular; and said semblance/similarity of said traceswithin each cell is a function of at least time, the number of seismictraces within said analysis cell, and the apparent dip and apparent dipazimuth of said traces within said analysis cell.
 16. The method ofclaim 15, where said semblance/similarity of said traces within eachcell is determined by computing a plurality of measurements of thesemblance/similarity of said traces within each cell and selecting thelargest of said measurements of said semblance/similarity of each cell;and wherein step (c) further includes the step of defining the apparentdip and apparent dip azimuth corresponding to said largest of saidmeasurements to be an estimate of the true dip and an estimate of thetrue dip azimuth of the seismic traces within said analysis cell. 17.The method of claim 16, wherein each of said plurality of measurementsof said semblance/similarity of at least a function of the energy ofsaid traces; and wherein said energy of said traces is a function oftime, the number of seismic traces within said analysis cell, and theapparent dip and apparent dip azimuth of said traces within saidanalysis cell.
 18. The method of claim 16, wherein said map is a colormap that is characterized by hue, saturation and lightness; wherein oneof said estimates of true dip azimuth, said estimates of true dip, andsaid largest calculated measures of semblance is mapped onto one of alightness scale, hue scale, and a saturation scale; wherein another ofsaid estimates of true dip azimuth, said estimates of true dip, and saidlargest calculated measures of semblance is mapped onto another of saidlightness scale, said hue scale, and said saturation scale; and whereinthe remaining one of said estimates of true dip azimuth, said estimatesof true dip, and said largest calculated measures of semblance is mappedonto the remaining one of said lightness scale, said hue scale, and saidsaturation scale.
 19. The method of claim 18, wherein step (d) comprisesthe steps of: mapping said estimates of true dip azimuth onto said huescale, mapping said estimates of true dip onto said saturation scale,and mapping said largest calculated measures of semblance onto alightness scale.
 20. In seismic exploration wherein 3D seismic datacomprising reflected seismic energy is recorded as a function of timeand wherein a computer is used that is programmed to process suchseismic traces and to produce an image therefrom that is representativeof subterranean features, an article of manufacture comprising: a mediumthat is readable by a computer and that carries instructions for saidcomputer to perform a process comprising the steps of: (a) accessing 3Dseismic data over a predetermined volume of the earth, said datacomprising seismic traces that are characterized by time, position andamplitude; and (b) ascertaining the similarity of nearby regions of said3D seismic data of said volume by: (1) dividing at least a portion ofsaid data into an array of relatively small, adjacent, three-dimensionalanalysis cells, wherein each of said analysis cells contains portions ofat least five seismic traces; and (2) computing a seismic attribute foreach cell that is a function of the largest of a plurality ofmeasurements of semblance and the corresponding apparent dip and thecorresponding apparent dip azimuth.
 21. The article of manufacture ofclaim 20, wherein said medium carries instructions for the computer toperform step (2) by making measurements of semblance that are a functionof:$\sum\limits_{j = 1}^{J}\quad {{u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}.}$

where x and y are distances measured from the center of the analysiscell along mutually perpendicular x and y axes, where J traces is thenumber of seismic traces, where U_(j)(π,p,q) represents a seismic trace,where π is the time, p is the apparent dip in the x direction, and q isthe apparent dip in the y direction; and wherein p and q are measured inms/meter.
 22. The article of manufacture of claim 21, wherein saidmedium carries instructions for the computer to perform step (2) bymaking measurements of the semblance that are also a function of:$\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}} \right\}^{2}.$


23. The article of manufacture of claim 21, wherein said medium carriesinstructions for said computer to perform step (1) by forming analysiscells having an elliptical cross-section.
 24. The article of manufactureof claim 23, wherein said predetermined volume is characterized by afracture having an ascertainable direction; and wherein said mediumcarries instructions for said computer to form analysis cells that aregenerally elliptical in shape and that have major axes aligned in thedirection of said fracture.
 25. In seismic exploration wherein reflectedseismic energy is recorded as a function of time to produce a series ofseismic traces, a method comprising the steps of: (a) accessing a dataset of seismic traces distributed over a three-dimensional volume of theearth, said volume of the earth having subterranean featurescharacterized by dip and dip azimuth; (b) calculating a plurality ofmeasures of the semblance of said traces within a relatively small threedimensional analysis cell that is located within said volume and at onepart of a predetermined time layer, wherein each measure of semblance isat least a function of time, the number of seismic traces within saidanalysis cell, and the apparent dip and apparent dip azimuth of saidtraces within said analysis cell; (c) computing a seismic attribute forsaid analysis cell that is at least a function of the largest of saidplurality of calculated measures of semblance and the correspondingapparent dip and the corresponding apparent dip azimuth, wherein saidcorresponding apparent dip and said corresponding apparent dip azimuthare defined to be estimates of the true dip and an estimate of the truedip azimuth of the seismic traces within said analysis cell; (d)repeating steps (b) and (c) along other parts of said time layer; and(e) forming a map of said seismic attributes over said time layer. 26.The method of claim 25, wherein step (a) comprises the steps of: (1)accessing 3D seismic data over a predetermined volume of the earth, said3D seismic data comprising at least eleven seismic traces that arecharacterized by time, position and amplitude; and (2) dividing aportion of said volume into at least one time layer comprising an arrayof relatively small, three-dimensional cubes that contain at least fiveseismic traces; and wherein said cubes are used as the cells to performstep (b).
 27. The method of claim 26, where in performing step (b) eachmeasure of semblance is a function of:$\frac{\left\{ {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right\}^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}}$

where each analysis cell contains portions of at least J seismic traces,where J is at least 5, where x and y are distances measured from thecenter of the analysis cell along mutually perpendicular x and y axes,where p and q are the apparent dips in the x and y directions, whereu_(j)(t,p,q,x,y) represents a seismic trace within said analysis cell,and where the true dip d and dip azimuth φ are related to p and q by p=dsin (φ) and q=d cos (φ).
 28. The method of claim 27, wherein eachmeasure of semblance for each dip, dip azimuth, and analysis point aresmoothed by forming a running window time integration over partial sumsof a time window within said horizontal time layer.
 29. A method ofseismic exploration, comprising the steps of: (a) reading a 3D seismicdata set comprising seismic signal traces that are distributed over avolume of the earth; (b) selecting at least one horizon slice from saidvolume and forming therein cells that are arranged into laterallyextending rows and columns, each of said cells having at least fiveseismic traces extending generally therethrough; (c) computing for eachof said cells; (1) a plurality of semblance measurements of said traces,wherein each measurement is at least a function of time, the number ofseismic traces within said analysis cell, and the apparent dip andapparent dip azimuth of said traces; (2) the largest of said pluralityof measurements of semblance; and (3) an estimate of the true dip and anestimate of the true dip azimuth of the seismic traces within saidanalysis cell from the apparent dip and apparent dip azimuthcorresponding to said largest measurement of semblance; and (d)displaying, over said at least one horizon slice, of representations ofsaid largest measurements of semblance and said estimated true dips andsaid estimated true dip azimuths of each of said cells.
 30. The methodof claim 29, wherein step (b) is performed by selecting a horizon slicethat is characterized by a common time; and wherein step (d) isperformed by displaying across said time slice representations of saidlargest measurements of semblance and said estimated true dips and saidestimated true dip azimuths of said cells.
 31. The method of claim 29,wherein step (d) is performed by forming a color map that ischaracterized by hue, saturation and lightness, wherein for each of saidcells: one of said estimates of true dip azimuth, said estimates of truedip, and said largest calculated measurements of semblance is mappedonto one of a lightness scale, hue scale, and a saturation scale;wherein another of said estimates of true dip azimuth, said estimates oftrue dip, and said largest calculated measurements of semblance ismapped onto another of said lightness scale, said hue scale, and saidsaturation scale; and wherein the remaining one of said estimates oftrue dip azimuth, said estimates of true dip, and said largestcalculated measurements of semblance is mapped onto the remaining one ofsaid lightness scale, said hue scale, and said saturation scale.
 32. Inthe exploration for gas and oil wherein over a volume of the earthseismic traces are recorded, a method comprising the steps of: (a)grouping at least parts of at least five relatively close seismic tracesinto a plurality of relatively small three-dimensional analysis cells;(b) performing in each of said cells a plurality of measurements of thesemblance of said parts of said traces as a function of at least time,the number traces therein, the apparent dip of said traces, and theapparent dip azimuth; (c) identifying in each of said cells the largestof said plurality of measurements of semblance, the correspondingapparent dip, and the corresponding dip azimuth; and (d) converting saidlargest measurements of semblance, said corresponding dip and saidcorresponding dip azimuth of said cells into color attributes of hue,saturation and lightness, wherein for each cell: one of said dipazimuth, said dip, and said largest measurements of semblance is mappedonto one of a lightness scale, hue scale, and a saturation scale;another of said dip azimuth, said dip, and said largest measurements ofsemblance is mapped onto another of said lightness scale, said huescale, and said saturation scale; and the remaining one of said dipazimuth, said dip, and said largest measurements of semblance is mappedon the remaining one of said lightness scale, said hue scale, and saidsaturation scale.
 33. A device adapted for use by a workstation wherein3D seismic data is read into memory and processed into a color displayof subterranean features, comprising: computer readable means carryinginstructions for a process comprising the steps of: (1) digitallylocating said 3D seismic data in an array of relatively smallthree-dimensional cells, wherein each of said cells containsrepresentations of a part of at least five seismic traces; (2)calculating for each of said cells an estimate of the semblance, andestimate of the true dip, and an estimate of the true dip azimuth ofsaid parts; and (3) converting said estimates of semblance, saidestimates of true dip, and said estimates of true dip azimuth into anarray of digital values corresponding to the color attributes of hue,saturation, and lightness.
 34. The device of claim 33, wherein one ofsaid estimates of true dip azimuth, said estimates of true dip, and saidestimates of semblance is mapped onto one of a lightness scale, a huescale, and a saturation scale for each of said cells; wherein another ofsaid estimates of true dip azimuth, said estimates of true dip, and saidestimates of semblance is mapped onto another of said lightness scale,said hue scale, and said saturation scale for each of said cells; andwherein the remaining one of said estimates of true dip azimuth, saidestimates of true dip, and said estimates of semblance is mapped ontothe remaining one of said lightness scale, said hue scale, and saidsaturation scale for each of said cells.
 35. The device of claim 33,wherein said computer readable means carries instructions to performstep (2) by: (i) calculating a plurality of semblance measurementsrelative to at least two directions, and selecting the largest of saidmeasurements; (ii) selecting the apparent dip corresponding to saidlargest measurement of semblance from step (i); and (iii) selecting theapparent dip azimuth corresponding to said largest measurement ofsemblance from step (i).
 36. The device of claim 33, wherein saidcomputer-readable means is selected from the group consisting of amagnetic tape, a magnetic disk, an optical disk and a CD-ROM.
 37. Amethod of prospecting for hydrocarbon deposits, comprising the steps of:(a) obtaining a color seismic attribute display of 3D seismic data for apredetermined three-dimensional volume of the earth, said display beinggenerated by using data obtained by a computer and at least one programfor said computer that instructs said computer to perform the followingsteps: (1) convert said volume into an array of relatively smallthree-dimensional cells, wherein each of said cells has a portion of atleast five seismic traces located therein; (2) make plurality ofsemblance measurements within each of said cells, wherein eachmeasurement is at least a function of time, the number of seismic traceswithin said cell, the apparent dip of said traces and apparent dipazimuth of said traces; (3) select the largest of said plurality ofmeasurements of semblance in each cell; (4) use as an estimate of thetrue dip and an estimate of the true dip azimuth in each cell theapparent dip and apparent dip azimuth that correspond to said largestmeasurement of semblance in said cell; (5) map said estimates of truedip azimuth onto a hue scale; (6) map said estimates of true dip onto asaturation scale; and (7) map said largest calculated measures ofsemblance onto a lightness scale; and (b) using said color display toidentify subsurface structural and sedimentological features commonlyassociated with the entrapment and storage of hydrocarbons.
 38. Themethod of claim 37, further including the step of using said map toidentify drilling hazards.
 39. The method of claim 38, further includingthe step of drilling at a location identified in step (b).
 40. Themethod of claim 37, wherein step (a)(2) comprises the step of computing:$\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}$

where each cell is characterized by two perpendicular dimensions, wherex and y are distances measured from the center of the cell alongmutually perpendicular x and y axes, where J is the number of seismictraces, where U_(j)(τ,p,q) represents a seismic trace, where τ is thetime, p is the apparent dip in the x direction, and where q is theapparent dip in the y direction.
 41. The method of claim 40, whereinstep (a)(2) comprises the step of computing:$\left\{ {\sum\limits_{j = 1}^{J}\quad {u\left\lbrack {\tau - \left( {{px}_{j} + {qy}_{j}} \right)} \right\rbrack}} \right\}^{2}.$


42. In a computer workstation wherein 3-D seismic data obtained over apredetermined three-dimensional volume of the earth is read into memory,wherein a computer divides such volume into an array ofthree-dimensional analysis cells, wherein each cell has at least aportion of five laterally separated seismic traces located therein, andwherein the computer is used to transform such data into a display ofseismic attributes, the computer CHARACTERIZED BY performing a processcomprising the steps of: (1) calculating in each of the cells asemblance value for said seismic traces, wherein said semblance value isat least a function of time, the number of seismic traces within saidcell, the apparent dip of said traces, and the apparent dip azimuth ofsaid traces; and (2) displaying said semblance value of each cell thatlies between two planes within the 3-D volume to identify subsurfacefeatures commonly associated with the entrapment and storage ofhydrocarbons.
 43. The computer workstation of claim 42, wherein thecomputer performs step (1) by: making a plurality of semblancemeasurements within each of said cells; and selecting the largest ofsaid plurality of measurements as said semblance value of said cell. 44.The computer workstation of claim 43, wherein after performing step (1)the computer performs the step of: using the apparent dip and theapparent dip azimuth that correspond to said largest measurement ofsemblance in said cell as an estimate of true dip and as an estimate oftrue dip azimuth of said cell.
 45. The computer workstation of claim 44,wherein the display of step (2) is characterized by color components ofhue, saturation and lightness; and wherein step (2) comprises the stepsof mapping said estimate of true dip azimuth for each cell onto a huescale; mapping said estimate of true dip for each dell onto a saturationscale; and mapping said largest calculated measures of semblance onto alightness scale.
 46. The method of claim 14 wherein saidsemblance/similarity is at least a function of time, amplitude and thenumber of traces within said cells.
 47. The method of claim 14 whereinsaid semblance/similarity is determined for data samples of a constanttime value.
 48. The method of claim 14 wherein said semblance/similarityis at least a function of:$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 49. The method of claim 48 wherein said semblance/similarityis a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


50. The method of claim 49 wherein said semblance/similarity is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


51. The method of claim 48 wherein said semblance/similarity isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 52. The methodof claim 14 wherein said semblance/similarity is at least a function of:$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, where pand q are apparent dips in the x and y directions, respectively, andwhere u _(f) (t,p,q,x _(j) ,y _(j) ) is a portion of a seismic tracewith said cell.
 53. The method of claim 52 wherein saidsemblance/similarity is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}}.$


54. The method of claim 52 wherein said semblance/similarity is anarithmetic inverse function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}}.$


55. The method of claim 52 wherein said semblance/similarity isdetermined by performing a running window time integration over partialsums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,p,q,x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 56. The methodof claim 52 wherein p=0 and q=0.
 57. The method of claim 14 wherein step(d) includes recording said semblance/similarity in a form for displaymapped to at least one of: (i) a lightness scale (ii) a hue scale, and,(iii) a saturation scale.
 58. In seismic exploration wherein 3D seismicdata from geologic formations of the earth are recorded as a function oftime and wherein a computer is used that is programmed to process such3D seismic data so that an image may be produced therefrom that isrepresentative of subterranean features, an article of manufacturecomprising: a medium that is readable by a computer and that carriesinstructions for said computer to perform a process comprising: (a)accessing 3D seismic data over a predetermined volume of geologicformations the earth, said 3D seismic data comprising seismic tracesthat are characterized by time, position and amplitude; and (b)ascertaining a seismic attribute of said 3D seismic data by: (1)dividing at least a portion of said 3D seismic data into a plurality ofrelatively small three-dimensional analysis cells, wherein each of saidanalysis cells contain portions of at least five seismic traces; and (2)computing a seismic attribute that is a function of semblance for eachanalysis cell.
 59. The article of manufacture of claim 58 wherein saidsemblance is at least a function of:$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 60. The article of manufacture of claim 59 wherein saidsemblance is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


61. The article of manufacture of claim 59 wherein said semblance is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


62. The article of manufacture of claim 59 wherein said semblance isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 63. The articleof manufacture of claim 58 wherein the process performed by the computerfurther comprises displaying said seismic attribute in a form that is atleast one of (i) a planar display, (ii) a cross-sectional display, (iii)a 2D display and, (iv) a 3D display.
 64. The article of manufacture ofclaim 58 wherein the computed seismic attribute is a number that is atleast 0 and at most
 1. 65. The article of manufacture of claim 58wherein the process further comprises displaying the computed seismicattributes in a visual format to display the subterranean features. 66.The article of manufacture of claim 65 wherein the visual format todisplay the subterranean features is at least one of (i) a cube ofdiscontinuity values, (ii) a cube of dissimilarity values, (iii) a cubeof semblance values, (iv) a cube of the inverse of semblance values, and(v) a cube of coherence values.
 67. In seismic exploration wherein 3Dseismic data from geologic formations of the earth are recorded as afunction of time and wherein a computer is used that is programmed toprocess such 3D seismic data so that an image may be produced therefromthat is representative of subterranean features, an article ofmanufacture comprising: a medium that is readable by a computer and thatcarries instructions for said computer to perform a process comprising:(a) accessing 3D seismic data over a predetermined volume of geologicformations of the earth, said 3D seismic data comprising seismic tracesthat are characterized by time, position and amplitude; and (b) dividingat least a portion of said data into a plurality of relatively small,three-dimensional analysis cells, wherein each of said three-dimensionalanalysis cells contains portions of at least five seismic traces; and(c) computing a seismic attribute for each cell that is a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 68. The article of manufacture of claim 67 wherein saidseismic attribute is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


69. The article of manufacture of claim 67 wherein said seismicattribute is an arithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


70. The article of manufacture of claim 67 wherein said seismicattribute is determined by performing a running window time integrationover the partial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 71. The articleof manufacture of claim 67 wherein the process performed by the computerfurther comprises displaying said seismic attribute in a form that is atleast one of (i) a planar display, (ii) a cross-sectional display, (iii)a 2D display and, (iv) a 3D display.
 72. The article of manufacture ofclaim 67 wherein the computed seismic attribute is a number that is atleast 0 and at most
 1. 73. The article of manufacture of claim 67wherein the process further comprises displaying the computed seismicattributes in a visual format to display the subterranean features. 74.A method for locating geologic features of an earth volume, the methodcomprising: (a) accessing 3D seismic data over a predetermined volume ofthe earth, said data comprising seismic traces that are characterized bytime, position and amplitude; (b) dividing at least a portion of said 3Dismic data into a plurality of relatively small, three-dimensionalanalysis cells, wherein each of said analysis cells contains portions ofat least five seismic traces; and (c) computing a seismic attribute foreach cell that is a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 75. The method of claim 74 wherein said seismic attribute isa function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


76. The method of claim 74 wherein said seismic attribute is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


77. The method of claim 74 wherein said seismic attribute is determinedby performing a running window time integration over the partial sumsfrom −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 78. The methodof claim 74 further comprising displaying said seismic attribute in aform that is at least one of (i) a planar display, (ii) across-sectional display, (iii) a 2D display, and (iv) a 3D display. 79.The method of claim 74 wherein the computed seismic attribute is anumber that is at least 0 and at most
 1. 80. The method of claim 74further comprising displaying the computed seismic attributes in avisual format to display the subterranean features.
 81. A method oflocating subterranean features, the method comprising: (a) accessing 3Dseismic data covering a pre-determined volume of the earth; (b) dividingsaid volume into an array of relatively small three- dimensional cellswherein each of said cells is characterized by at least five laterallyseparated and generally vertical seismic traces located therein; (c)determining in each of said cells a semblance/similarity of said traces;and (d) recording said semblance/similarity of said cells.
 82. Themethod of claim 81 wherein said semblance/similarity is at least afunction of:$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 83. The method of claim 82 wherein said semblance/similarityis a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


84. The method of claim 82 wherein said semblance/similarity is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


85. The method of claim 82 wherein said semblance/similarity isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 86. The methodof claim 81 further comprising displaying said semblance/similarity in aform that is at least one of (i) a planar display, (ii) across-sectional display, (iii) a 2D display, and (iv) a 3D display. 87.The method of claim 81 wherein the semblance/similarity of said tracesis a number that is at least 0 and at most
 1. 88. The method of claim 81further comprising displaying the semblance/similarity in a visualformat to display the subterranean features.
 89. A method of locatinggeologic formations, the method comprising: (a) accessing 3D seismicdata covering a pre- determined volume of the earth; (b) dividing saidvolume into an array of relatively small three-dimensional cells whereineach of said cells is characterized by at lest five laterally separatedand generally vertical seismic traces located therein; (c) determiningin each of said cells an inverse of a semblance/similarity of saidtraces relative to two pre-determined directions; and (d) recording saidinverse of said semblance/similarity of said cells.
 90. The method ofclaim 89 wherein the inverse of said semblance/similarity is an additiveinverse.
 91. A method of generating a discontinuity cube for displayingsubterranean geologic features of a volume of earth formation, themethod comprising: (a) accessing 3D seismic data covering apre-determined volume of the earth; (b) dividing said volume into anarray of relatively small three-dimensional cells wherein each of saidcells is characterized by at least five laterally separated andgenerally vertical seismic traces located therein; (c) assigning asignal discontinuity value to each said cell; and (d) assigning a uniquecolor to each said signal discontinuity value in said cells.
 92. Themethod of claim 91 wherein the signal discontinuity value is at least afunction of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(f) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 93. The method of claim 92 wherein said signal discontinuityvalue is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


94. The method of claim 92 wherein said signal discontinuity value is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}}.$


95. The method of claim 92 wherein said signal discontinuity value isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad \Delta \quad t}},x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}{\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {{t + {k\quad {\Delta t}}},x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 96. The methodof claim 91 further comprising displaying said signal discontinuityvalue in a form that is at least one of (i) a planar display, (ii) across-sectional display, (iii) a 2D display, and (iv) a 3D display. 97.The method of claim 91 wherein the signal discontinuity value of saidcells is a number that is at least 0 and at most
 1. 98. The method ofclaim 91 further comprising displaying the signal discontinuity value ina visual format to display the subterranean features.
 99. A method ofgenerating a cube for displaying geologic features, faults and contoursof a cubic volume of an earth formation wherein 3D seismic data samplescovering said cubic volume of the earth formation are accessed, saidcubic volume of the earth formation divided into an array of relativelysmall 3D cells containing at least a portion of the 3D seismic datasamples, the cube representing said cubic volume of said earth formationenclosing a plurality of the 3D seismic data samples, the methodcomprising: (a) assigning a semblance value to each seismic data samplein said cube; and (b) assigning a unique color to each semblance valuein said cube.
 100. The method of claim 99 wherein the semblance value isat least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and whereu_(f(t,x) _(j) ,y _(j) ) is a portion of a seismic trace within saidcell.
 101. A method of generating a cube for displaying a set ofgeologic features, faults and contours of a cubic volume on an earthformation wherein a plurality of 3D seismic data samples covering saidcubic volume of the earth formation is accessed, said cubic volume ofthe earth formation divided into an array of relatively small 3D cells,said cube representing said cubic volume of said earth formationenclosing at least a portion of said plurality of 3D seismic datasamples, the method comprising: (a) assigning an inverse of semblancevalue to each seismic data sample in said cube; and (b) assigning aunique color to each said inverse of semblance value in said cube. 102.The method of claim 101 wherein the inverse of semblance value is atleast a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(f)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.103. The method of claim 101 wherein said inverse of semblance value isan additive inverse.
 104. A method of generating a cube for displaying aset of geologic features, faults and contours of a cubic volume of anearth formation wherein 3D seismic data samples covering said cubicvolume of the earth formation are accessed, said cubic volume of theearth formation divided into an array of relatively small 3D cellscontaining at least a portion of the 3D seismic data samples, said cuberepresenting said cubic volume of said earth formation enclosing atleast a portion of a plurality of the 3D seismic data samples, themethod comprising the steps of: (a) mapping a semblance value to eachseismic data sample in said cube; and (b) mapping a unique color to eachsemblance value in said cube.
 105. The method of claim 104 wherein thesemblance value is at least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(f)(t,x, _(j) ,y _(j) ) is a portion of a seismic trace within said cell.106. A method of generating a cube for displaying geologic features,faults and contours of a volume of an earth formation wherein aplurality of seismic data samples covering the volume of the earthformation is accessed, said volume of the earth formation divided intoan array of relatively small three-dimensional cells, said cellscharacterized by at least five laterally separated and generallyvertical seismic traces located therein, the method comprising: (a)assigning a signal discontinuity value to each seismic data sample insaid cube; and (b) assigning a unique color to each said signaldiscontinuity value in said cube.
 107. The method of claim 106 whereinthe signal discontinuity value is at least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) eismic traces,where x and y are distances from the center of the cell, and where u_(f)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.108. A method to generate a coherence cube for locating subterraneanfeatures, faults, and contours, the method comprising: (a) accessing 3Dseismic data covering a pre-determined volume of the earth; (b) dividingsaid volume into an array of relatively small three-dimensional cellswherein each of said cells is characterized by at least five laterallyseparated and generally vertical seismic traces located therein; (c)determining in each of said cells the ratio of incoherent energy andcoherent energy of said traces relative to two pre-determineddirections; and (d) recording said ratio of incoherent energy andcoherent energy of said cells in a form for display as a map ofsubterranean features.
 109. A method of locating subterranean features,faults, and contours, the method comprising: (a) accessing 3D seismicdata covering a pre-determined volume of the earth; (b) dividing saidvolume into an array of relatively small three-dimensional cells whereineach of said cells is characterized by at least five laterally separatedand generally vertical seismic traces located therein; (c) determiningin each of said cells the discontinuity/dissimilarity of said tracesrelative to two pre-determined directions; and (d) recording saiddiscontinuity/dissimilarity of said cells in a form for display as a mapof subterranean features.
 110. The method of claim 109 wherein thediscontinuity/dissimilarity is at least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(f)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.111. A device adapted for use by a workstation wherein 3D seismic datais read into memory and processed into a color display of subterraneanfeatures, the device including computer readable means carryinginstructions for a process comprising: (a) digitally locating said 3Dseismic data in an array of relatively small three-dimensional cells,wherein each of said cells contains representations of a part of atleast five seismic traces; (b) calculating for each of said cells anestimate of the semblance; and (c) converting said estimate of semblanceinto an array of digital values corresponding to color attributes. 112.The device of claim 111, wherein said computer-readable means isselected from the group consisting of a magnetic tape, a magnetic disk,an optical disk and a CD-ROM.
 113. The device of claim 111 furthercomprising means for displaying the computed estimates of semblance in avisual format of subterranean features.
 114. In a computer workstationwherein 3D seismic data obtained over a predetermined three-dimensionalvolume of the earth is read into memory, wherein a computer divides suchvolume into an array of three-dimensional analysis cells, wherein eachcell has at least a portion of five laterally separated seismic traceslocated therein, and wherein the computer is used to transform such datainto a display of seismic attributes, the computer CHARACTERIZED BYperforming a process comprising: (a) calculating in each of the cells asemblance value for said seismic traces, wherein said semblance value isat least a function of amplitude, time, and the number of seismic traceswithin said cell; and (b) displaying said semblance value of each cellwithin the 3D volume to identify subsurface features commonly associatedwith the entrapment and storage of hydrocarbons.
 115. The computerworkstation of claim 114, wherein the display of step (b) ischaracterized by color components of at least one of hue, saturation andlightness, and wherein step (b) comprises mapping said semblance foreach cell onto one of (i) a hue scale, (ii) a saturation scale, and(iii) a lightness scale.
 116. A method of seismic exploration forlocating geologic formations, faults, contours and unconformities, themethod comprising: (a) reading a 3D seismic data set comprising seismicsignal traces that are distributed over a volume of the earth; (b)selecting at least one time slice from said volume and forming thereincells that are arranged into laterally extending rows and columns, eachof said cells having at least five seismic traces therein; (c) computingfor each of said cells a plurality of semblance measurements of saidtraces, wherein each measurement is at least a function of amplitude,time, and the number of seismic traces within said cell; and (d)recording in a form for display, over said at least one time slice,measurements of semblance.
 117. A method of seismic exploration forlocating geologic formations, faults, contours and unconformities, themethod comprising: (a) reading a 3D seismic data set comprising seismicsignal traces that are distributed over a volume of the earth; (b)selecting at least one time slice from said volume and forming thereincells that are arranged into laterally extending rows and columns, eachof said cells having at least five seismic traces therein; (c) computingfor each of said cells at least one seismic attribute wherein said atleast one seismic attribute is at least a function of:$\left( {\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{f}\left( {t,x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell and where u_(f)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell;and (d) recording in a form for display, over said at least one timeslice, said at least one seismic attribute.
 118. The method of claim 14wherein said semblance/similarity is at least a function of:$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 119. The method of claim 118 wherein saidsemblance/similarity is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


120. The method of claim 119 wherein said semblance/similarity is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


121. The method of claim 118 wherein said semblance/similarity isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 122. The methodof claim 14 wherein said semblance/similarity is at least a function of:$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, where pand q are apparent dips in the x and y directions, respectively, andwhere u_(j) (t,p,q,x _(j) ,y _(j) ) is a portion of a seismic tracewithin said cell.
 123. The method of claim 122 wherein saidsemblance/similarity is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


124. The method of claim 122 wherein said semblance/similarity is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


125. The method of claim 122 wherein said semblance/similarity isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 126. The methodof claim 122 wherein p=0 and q=0.
 127. The article of manufacture ofclaim 58 wherein said semblance is at least a function of:$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 128. The article of manufacture of claim 127 wherein saidsemblance is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


129. The article of manufacture of claim 127 wherein said semblance isan arithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


130. The article of manufacture of claim 127 wherein said semblance isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 131. In seismicexploration wherein 3D seismic data from geologic formations of theearth are recorded as a function of time and wherein a computer is usedthat is programmed to process such 3D seismic data so that an image maybe produced therefrom that is representative of subterranean features,an article of manufacture comprising: a medium that is readable by acomputer and that carries instructions for said computer to perform aprocess comprising: (a) accessing 3D seismic data over a predeterminedvolume of geologic formations of the earth, said 3D seismic datacomprising seismic traces that are characterized by time, position andamplitude; (b) dividing at least a portion of said data into a pluralityof relatively small, three-dimensional analysis cells, wherein each ofsaid three-dimensional analysis cells contains portions of at least fiveseismic traces; and (c) computing a seismic attribute for each cell thatis a function of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 132. The article of manufacture of claim 131 wherein saidseismic attribute is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


133. The article of manufacture of claim 131 wherein said seismicattribute is an arithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


134. The article of manufacture of claim 131 wherein said seismicattribute is determined by performing a running window time integrationover the partial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 135. Thearticle of manufacture of claim 131 wherein the process performed by thecomputer further comprises displaying said seismic attribute in a formthat is at least one of (i) a planar display, (ii) a cross-sectionaldisplay, (iii) a 2D display and, (iv) a 3D display.
 136. The article ofmanufacture of claim 131 wherein the computed seismic attribute is anumber that is at least 0 and at most
 1. 137. The article of manufactureof claim 131 wherein the process further comprises displaying thecomputed seismic attributes in a visual format to display thesubterranean features.
 138. A method for locating geologic features ofan earth volume, the method comprising: (a) accessing 3D seismic dataover a predetermined volume of the earth, said data comprising seismictraces; (b) dividing at least a portion of said 3D seismic data into aplurality of relatively small, three-dimensional analysis cells, whereineach of said analysis cells contains portions of at least five seismictraces relative to two directions; and (c) computing a seismic attributefor each cell that is a function of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 139. The method of claim 138 wherein said seismic attributeis a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


140. The method of claim 138 wherein said seismic attribute is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


141. The method of claim 138 wherein said seismic attribute isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 142. The methodof claim 138 further comprising displaying said seismic attribute in aform that is at least one of (i) in planar display, (ii) across-sectional display, (iii) a 2D display, and (iv) a 3D display. 143.The method of claim 138 wherein the computed seismic attribute is anumber that is at least 0 and at most
 1. 144. The method of claim 138further comprising displaying the computed seismic attributes in avisual format to display the subterranean features.
 145. The method ofclaim 81 wherein said semblance/similarity is at least a function of:$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 146. The method of claim 145 wherein saidsemblance/similarity is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


147. The method of claim 145 wherein said semblance/similarity is anarithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


148. The method of claim 145 wherein said semblance/similarity isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 149. The methodof claim 91 wherein the signal discontinuity value is at least afunction of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each analysis cell contains portions of at least J (J≧5) seismictraces, where x and y are distances from the center of the cell, andwhere u_(j) (t,x _(j) ,y _(j) ) is a portion of a seismic trace withinsaid cell.
 150. The method of claim 149 wherein said signaldiscontinuity value is a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


151. The method of claim 149 wherein said signal discontinuity value isan arithmetic inverse of a function of:$\frac{\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


152. The method of claim 149 wherein said signal discontinuity value isdetermined by performing a running window time integration over thepartial sums from −K to +K:$\frac{\sum\limits_{k = {- K}}^{+ K}\quad \left( {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{k = {- K}}^{+ K}\quad {\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {{t + {k\Delta t}},\quad x_{j},y_{j}} \right)}^{2}}}$

where K is the half width of the time window in samples.
 153. The methodof claim 99 wherein the semblance value is at least a function of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.154. The method of claim 101 wherein the inverse of semblance value isat least a function of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.155. The method of claim 104 wherein the semblance value is at least afunction of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.156. The method of claim 106 wherein the semblance value is at least afunction of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.157. The method of claim 109 wherein the discontinuity/dissimilarity isat least a function of$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell.158. A method of seismic exploration for locating geologic formations,faults, contours and unconformities, the method comprising: (a) readinga 3D seismic data set comprising seismic signal traces that aredistributed over a volume of the earth; (b) selecting at least one timeslice from said volume and forming therein cells that are arranged intolaterally extending rows and columns, each of said cells having at leastfive seismic traces therein; (c) computing for each of said cells atleast one seismic attribute wherein said at least one seismic attributeis at least a function of:$\left( {\sum\limits_{j = 1}^{J}{\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {u_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) seismic traces,where x and y are distances from the center of the cell, and where u_(j)(t,x _(j) ,y _(j) ) is a portion of a seismic trace within said cell;and (d) recording in a form for display, over said at least one timeslice, said at least one seismic attribute.
 159. The method of claim 74further comprising storing said seismic attributes for each cell as adata set.
 160. The method of claim 81 further comprising storing saidsemblance/similarity of said cells as a data cube.
 161. The method ofclaim 89 further comprising storing said inverse of saidsemblance/similarity of said cells as a data cube.
 162. The method ofclaim 138 further comprising storing said seismic attributes for eachcell as a data cube.
 163. A method of generating a data cube fordisplaying geologic features, faults and contours of a cubic volume ofan earth formation wherein 3D seismic data samples covering said volumeof the earth formation are accessed, said volume of the earth formationdivided into an array of relatively small 3D cells containing at least aportion of the 3D seismic data samples relative to two spatialdirections, the cube of semblance/similarity values representing saidvolume of said earth formation enclosing a plurality of the 3D seismicdata samples, the cube of semblance/similarity values formed by: (a)forming an analytic trace from each seismic trace; and (b) assigning asemblance/similarity value to each analytic trace data sample in saidcube.
 164. The method of claim 163 wherein said analytic trace, v_(j)(t), is a function of u _(j) (t) +iu _(j) ^(H) (t).
 165. A method forcreating an analytic coherence cube of semblance/similarity values, themethod comprising: (a) accessing 3D seismic data covering apre-determined volume of the earth; (b) forming an analytic trace fromeach seismic trace; (c) dividing said volume into an array of relativelysmall three-dimensional cells wherein each of said cells ischaracterized by at least five laterally separated and generallyvertical analytic traces located therein; (d) determining in each ofsaid cells the semblance/similarity of said analytic traces relative totwo pre-determined directions; and (e) recording an analytic coherencecube from said semblance/similarity of said cells.
 166. The method ofclaim 165 wherein said analytic traces, v_(j) (t,x _(j) ,y _(j) ), are afunction of u _(j) (t,x _(j) ,y _(j) ) +iu _(j) ^(H) (t,x _(j) ,y _(j)).
 167. The method of claim 165 wherein each semblance/similarity valueis at least a function of$\left( {\sum\limits_{j = 1}^{J}\quad {v_{j}\left( {t,\quad x_{j},y_{j}} \right)}} \right)^{2}$

and${\sum\limits_{j = 1}^{J}\quad {v_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}},$

where each cell contains portions of at least J (J≧5) analytic traces,where x and y are distances from the center of the cell, and where v_(j)(t,x _(j) ,y _(j) ) is a portion of an analytic trace within said cell.168. The method of claim 167 wherein said semblance/similarity is afunction of:$\frac{\left( {\sum\limits_{j = 1}^{J}\quad {v_{j}\left( {t,\quad x_{j},y_{j}} \right)}} \right)^{2}}{\sum\limits_{j = 1}^{J}\quad {v_{j}\left( {t,\quad x_{j},y_{j}} \right)}^{2}}.$


169. The method of claim 165, wherein said pre-determined directions aremutually perpendicular, and said semblance/similarity of said analytictraces within each cell is a function of at least time and the number ofanalytic traces within said analysis cell.
 170. The method of claim 165wherein said semblance/similarity is at least a function of the energyof said analytic traces; and wherein said energy of said analytic tracesis a function of time, and the number of said analytic traces withinsaid cell.
 171. The method of claim 165 wherein saidsemblance/similarity is at least a function of the apparent dip andapparent dip azimuth of said analytic traces within said analysis cell.172. The method of claim 165, wherein said semblance/similarity of saidcells are characterized by hue, saturation and lightness; wherein one ofsaid estimates of true dip azimuth, said estimates of true dip, and saidlargest calculated measures of semblance is mapped onto one of alightness scale, hue scale, and a saturation scale; wherein another ofsaid estimates of true dip azimuth, said estimates of true dip, and saidlargest calculated measures of semblance is mapped onto another of saidlightness scale, said hue scale, and said saturation scale; and whereinthe remaining one of said estimates of true dip azimuth, said estimatesof true dip, and said largest calculated measures of semblance is mappedonto the remaining one of said lightness scale, said hue scale, and saidsaturation scale.
 173. The method claim 172, wherein said estimates oftrue dip azimuth are mapped onto said hue scale, said estimates of truedip are mapped onto said saturation scale, and said largest calculatedmeasures of semblance are mapped onto a lightness scale.
 174. A methodfor locating geologic features of an earth volume, the methodcomprising: (a) accessing 3D seismic data over a predetermined volume ofthe earth, said data comprising seismic traces that are characterized bytime, position and amplitude values; (b) dividing at least a portion ofsaid 3D seismic data into a plurality of relatively small,three-dimensional analysis cells, wherein each of said analysis cellscontains portions of at least five seismic traces relative to twodirections; (c) computing a seismic attribute for each cell that is afunction of i) the square of the sum of the seismic trace amplitudevalues for the at least five traces, and ii) the sum of the squares ofsaid seismic trace amplitude values for the at least five traces; and(d) recording said seismic attribute.
 175. A method of locatingsubterranean features, faults, and contours, comprising the steps of:(a) accessing 3D seismic data covering a pre-determined volume of theearth; (b) dividing said volume into an array of relatively smallthree-dimensional cells wherein each of said cells is characterized byat least five laterally separated and generally vertical seismic traceslocated therein; (c) determining in each of said cells asemblance/similarity of said traces relative to two pre-determineddirections; and (d) recording said semblance/similarity of said cells.